ArticlePLoS genetics2020
Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MMGS: a novel genomic prediction framework to integrate genotype, environment and their interactions for multi-environment breeding trials.Horticulture research · 2026Article
- The Interplay of Biotic and Abiotic Factors in Shaping Genetic Variation atEcology and evolution · 2025Article
- Genetic structure and triazole resistance amongmSphere · 2023Article
- Complex genetic architecture underlying the plasticity of maize agronomic traits.Plant communications · 2023Article
- The complex role of genetic background in shaping the effects of spontaneous and induced mutations.Yeast (Chichester, England) · 2021Review
- Associations betweenAging · 2021Article
- Article
- Dissecting the Genetic Regulation of Yeast Growth Plasticity in Response to Environmental Changes.Genes · 2020Article
- Gene-Environment Interaction in the Era of Precision Medicine - Filling the Potholes Rather Than Starting to Build a New Road.Frontiers in genetics · 2020Article
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Authors and funding
2 authors.
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Abstract
Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69-100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.
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